Swarmplots_Various_SuppFigs.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f1f56117",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "from matplotlib.pyplot import figure\n",
    "from collections import Counter\n",
    "from scipy.stats import spearmanr,pearsonr,fisher_exact,binom_test\n",
    "import scanpy as sc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be75e7ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Colors\n",
    "mouse = \"#F2C911\"\n",
    "rat = \"#65B0AC\"\n",
    "intrinsic = \"#F55F00\"\n",
    "extrinsic = \"#7D9AF4\"\n",
    "reinforcing = \"#9B00F5\"\n",
    "opposing = \"#F50901\"\n",
    "interaction = \"#1E771A\"\n",
    "\n",
    "sns.set(font_scale=1.5)\n",
    "sns.set_style(\"white\")\n",
    "d_ct_abrev = {\"Forebrain glutamatergic progenitors\":\"brain.glut.prog\", \"Forebrain GABAergic progenitors\":\"brain.GABA.prog\", \"Intermediate progenitors\":\"inter.prog\", \"Forebrain glutamatergic neurons\":\"brain.glut.neu\", \"Forebrain GABAergic neurons\":\"brain.GABA.neu\", \"Spinal GABAergic neurons\":\"spine.GABA.neu\", \"Spinal glutamatergic neurons\":\"spine.glut.neu\", \"Chondrocytes\":\"chondrocyte\", \"Mesenchyme 0\":\"mesen.0\", \"Mesenchyme 2\":\"mesen.2\", \"Mesenchyme cycling\":\"mesen.cyc\", \"Forebrain GABAergic neurons 0\":\"Toss\"}\n",
    "\n",
    "#Convert file name to cell type\n",
    "def file_to_celltype(x):\n",
    "    x = x.split(\"/\")[-1]\n",
    "    x = x.replace(\"_GO_Molecular_Function_2023\", \"\").replace(\"_GO_Biological_Process_2023\", \"\").replace(\"Mesechyme\", \"Mesenchyme\").replace(\"Brain_Or_2010_Div_New4_NewNorm_\", \"\")\n",
    "    x = x.replace(\"Mesenchymal_Or_2010_Div_New4_NewNorm_\", \"\").replace(\"Chondrocyte_Or_2010_Div_New4_NewNorm_\", \"\").replace(\".csv\", \"\").replace(\"_\", \" \").replace(\" all\", \"\")\n",
    "    x = x.replace(\"Glutamatergic\", \"Forebrain glutamatergic\").replace(\"GABAergic\", \"Forebrain GABAergic\").replace(\"Spinal Forebrain\", \"Spinal\").replace(\"Chondrocytes\", \"Chondrocyte\")\n",
    "    return x\n",
    "\n",
    "plt.rcParams[\"font.family\"] = \"Arial\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ad672eab",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Commented and uncommented this to get the right file and cell type\n",
    "\n",
    "excit = [0, 5]\n",
    "mes_0 = [0]\n",
    "inh_prog = [2]\n",
    "inhib_all = [0, 1, 3, 4]\n",
    "spinal_excit = [6]\n",
    "#brain = sc.read(\"Final/Brain_Subclustered_Prelim.h5ad\")\n",
    "#tp = brain[brain.obs[\"leiden\"].isin([str(x) for x in excit])]\n",
    "\n",
    "#chondro = sc.read(\"Final/Chondrocyte_Subclustered_Prelim.h5ad\")\n",
    "#tp = chondro[chondro.obs[\"leiden\"].isin([str(0)])]\n",
    "\n",
    "#mes = sc.read(\"Final/Mesenchymal_Subclustered_Prelim.h5ad\")\n",
    "#tp = mes[mes.obs[\"leiden\"].isin([str(0)])]\n",
    "\n",
    "inh = sc.read(\"Final/GABAergic_Forebrain.h5ad\")\n",
    "tp = inh[inh.obs[\"leiden\"].isin([str(x) for x in inhib_all])]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c90a025",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Changed tissue as appropriate to match above cell\n",
    "\n",
    "tissue = \"neur\"\n",
    "if tissue == \"conn\":\n",
    "    rm = \"RM2\"\n",
    "elif tissue == \"neur\":\n",
    "    rm = \"RM1\"\n",
    "\n",
    "#Convert to the nomenclature used in the publication\n",
    "#Subset anndata\n",
    "tp = tp[tp.obs[\"Sample\"].isin([rm, \"MR1\"])]\n",
    "\n",
    "#Add environment column\n",
    "tp.obs[\"Environment\"] = [\"Mouse-like env.\" if x == rm else \"Rat-like env.\" for x in list(tp.obs[\"Sample\"])]\n",
    "\n",
    "#Repeat for rm, mouse\n",
    "tp2 = tp[(tp.obs[\"Sample\"].isin([rm])) & (tp.obs[\"Species\"].isin([\"Mouse\"]))]\n",
    "tp2.obs[\"Environment\"] = np.repeat(\"Host\", tp2.obs.shape[0])\n",
    "tp = tp.concatenate([tp2])\n",
    "\n",
    "#Repeat for MR, rat\n",
    "tp2 = tp[(tp.obs[\"Sample\"].isin([\"MR1\"])) & (tp.obs[\"Species\"].isin([\"Rat\"]))]\n",
    "tp2.obs[\"Environment\"] = np.repeat(\"Host\", tp2.obs.shape[0])\n",
    "tp = tp.concatenate([tp2])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc9a7f2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Changed gene and cell_type as appropriate\n",
    "\n",
    "gene = \"Grb10\"\n",
    "cell_type = \"brain.GABA.neu\"\n",
    "\n",
    "#Convert to dataframe of counts\n",
    "tpx = pd.DataFrame(tp.X.todense())\n",
    "tpx.index = tp.obs.index\n",
    "tpx.columns = tp.var.index\n",
    "tpx = tpx.join(tp.obs)\n",
    "\n",
    "sns.set(font_scale=1.5)\n",
    "sns.set_style(\"white\")\n",
    "\n",
    "#Create swarmplot of non-zero log normalized counts\n",
    "sns.swarmplot(data = tpx[tpx[gene] > 0], y = gene, x = \"Environment\", hue = \"Species\", palette = {\"Mouse\":mouse, \"Rat\":rat}, alpha = 1, order = [\"Host\", \"Rat-like env.\", \"Mouse-like env.\"], dodge = True)\n",
    "plt.ylabel(\"Normalized counts per cell\")\n",
    "#plt.xlabel(\"Chimera (donor\" + r'$\\rightarrow$' + \"host)\")\n",
    "plt.xlabel(\"Extrinsic environment\")\n",
    "plt.title(\"Expression of $\\it{\" + gene + \"}$ in \" + cell_type)\n",
    "\n",
    "#Set boundaries and add the vertical line \n",
    "xmin, xmax, ymin, ymax = plt.axis()\n",
    "\n",
    "#plt.ylim(0, 1)\n",
    "plt.axvline(0.5, color = \"black\", linewidth = 2.5, alpha = 1, linestyle=\"dashed\")\n",
    "#plt.legend(bbox_to_anchor=(1.375, 1.05))\n",
    "plt.legend([],[], frameon=False)\n",
    "plt.ylim(0, ymax)\n",
    "\n",
    "#Compute percentage of cells with non-zero expression\n",
    "#c is the location that the text will go on the plot\n",
    "c = -0.33\n",
    "\n",
    "#For each environment\n",
    "for i in [\"Host\", \"Rat-like env.\", \"Mouse-like env.\"]:\n",
    "    #For each species\n",
    "    for j in [\"Mouse\", \"Rat\"]:\n",
    "        \n",
    "        #Restrict to that env species combination\n",
    "        tpx_cur = tpx[(tpx[\"Environment\"].isin([i])) & (tpx[\"Species\"].isin([j]))]\n",
    "        \n",
    "        #Get total number of cells\n",
    "        tot = tpx_cur.shape[0]\n",
    "        \n",
    "        #Get number of cells expressing\n",
    "        express = tpx_cur[tpx_cur[gene] > 0].shape[0]\n",
    "        \n",
    "        #Compute percentage\n",
    "        perc = int(round(express/tot, 2)*100)\n",
    "        \n",
    "        #Add text in appropriate place on the plot\n",
    "        plt.text(c, 0.125, str(perc) + \"%\")\n",
    "        \n",
    "        #Increment location\n",
    "        c += 0.4\n",
    "    #Increment location\n",
    "    c += 0.21\n",
    "    \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c9a3010",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Below is not used so is not commented ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c4088ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Alternative code to make half-violin/half stripplot\n",
    "sns.set(font_scale=1.5)\n",
    "sns.set_style(\"white\")\n",
    "ax = sns.violinplot(data = tpx, y = gene, x = \"Environment\", hue = \"Species\", palette = {\"Mouse\":mouse, \"Rat\":rat}, alpha = 1, order = [\"Host\", \"Rat-like env.\", \"Mouse-like env.\"], dodge = True)\n",
    "plt.ylabel(\"Normalized counts per cell\")\n",
    "#plt.xlabel(\"Chimera (donor\" + r'$\\rightarrow$' + \"host)\")\n",
    "plt.xlabel(\"Extrinsic environment\")\n",
    "plt.title(\"Expression of $\\it{\" + gene + \"}$ in \" + cell_type)\n",
    "xmin, xmax, ymin, ymax = plt.axis()\n",
    "\n",
    "#plt.ylim(0, 1)\n",
    "plt.axvline(0.5, color = \"black\", linewidth = 2.5, alpha = 1, linestyle=\"dashed\")\n",
    "#plt.legend(bbox_to_anchor=(1.375, 1.05))\n",
    "\n",
    "xlim = ax.get_xlim()\n",
    "ylim = ax.get_ylim()\n",
    "for violin in ax.collections:\n",
    "    bbox = violin.get_paths()[0].get_extents()\n",
    "    x0, y0, width, height = bbox.bounds\n",
    "    violin.set_clip_path(plt.Rectangle((x0, y0), width / 2, height, transform=ax.transData))\n",
    "\n",
    "old_len_collections = len(ax.collections)\n",
    "sns.stripplot(data = tpx, y = gene, x = \"Environment\", hue = \"Species\", palette = {\"Mouse\":mouse, \"Rat\":rat}, alpha = 1, order = [\"Host\", \"Rat-like env.\", \"Mouse-like env.\"], dodge = True)\n",
    "for dots in ax.collections[old_len_collections:]:\n",
    "    dots.set_offsets(dots.get_offsets() + np.array([0.12, 0]))\n",
    "ax.set_xlim(xlim)\n",
    "ax.set_ylim(ylim)\n",
    "plt.legend([],[], frameon=False)\n",
    "plt.show()"
   ]
  }
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